Systems Simulation

Course: Data science

Structural unit: Faculty of information Technology

Title
Systems Simulation
Code
ОК 28
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
7 Semester
Number of ECTS credits allocated
5
Learning outcomes
To use knowledge of regularities of random phenomena, their properties and operations on them, models of random processes and modern software environments to solve problems of statistical data processing and build predictive models. Use the methods of numerical differentiation and integration of functions, solving ordinary differential and integral equations, features of numerical methods and the possibilities of their adaptation to engineering problems, have skills in software implementation of numerical methods. Understand the principles of modeling organizational and technical systems and operations; use operations research methods, solving single- and multi-criteria optimization problems of linear, integer, nonlinear, stochastic programming.
Form of study
Full-time form
Prerequisites and co-requisites
Independent and dependent, discrete and continuous random variables. Distribution functions for discrete and continuous random variables. Types of distributions of random variables: uniform, indicative, normal, Poisson, Erlang and their main characteristics. Limit theorems of probability theory: law of large numbers, central limit theorem, Bernoulli's theorem. Random processes, properties of stationarity and ergodicity. Markov processes and their properties. Mass service systems and their varieties. A simple application flow and its properties. Sets, graphs and their varieties. Relations on sets. Statistical evaluation of parameters and distribution laws of random variables. Reliability of assessments and criteria of reliability. Design of experiments, variance and regression analyses.
Course content
In the process of studying the discipline "System Modeling", students must learn to use mathematical methods of analysis and modeling of information systems. Use modern systems modeling software, search for optimal solutions to practical problems and choose the best ways to implement these solutions The goal of the discipline is to provide students with theoretical knowledge of the methodology and technology of mathematical computer modeling in the process of designing, researching and operating complex systems; acquisition of practical skills in the use of mathematical modeling in tasks of analysis and synthesis of information systems and technologies.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
Lectures, practical activities, individual work
Assessment methods and criteria
For laboratory work and individual assignments for independent work that are not submitted within the time limits specified when the assignment is issued, the grade is reduced by 5% for every three days of delay (excluding Sundays). There are two written tests during the semester. The condition for obtaining a positive final grade in the discipline is to achieve at least 60% of the maximum possible number of points, while the grade for the learning outcomes provided for in paragraphs 2, 3 cannot be less than 50% of the maximum level. The maximum number of points that a student can receive for work during the semester is 60 points on a 100-point scale. The final assessment is an exam that is conducted in writing. The exam paper consists of two parts: test and analytical. The total score for the exam is 40 points on a 100-point scale, including 30 points for the test part and 10 points for the analytical part.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline